發(fā)布時(shí)間：2018-03-15 18:23

  本文選題：人工智能　切入點(diǎn)：模式識(shí)別　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著人們生活水平的提升和汽車總體造價(jià)的下降,汽車在人們的生活中扮演了越來(lái)越重要的角色。車輛在復(fù)雜道路狀況下的安全駕駛是一個(gè)重要的研究方向,道路信息檢測(cè)則是其中的關(guān)鍵內(nèi)容。然而,目前對(duì)道路信息檢測(cè)研究較少,特別是車輛作為節(jié)點(diǎn)主動(dòng)探測(cè)道路信息的研究較少。在此背景下,本課題研究了基于多傳感器的特定道路信息識(shí)別算法,研究?jī)?nèi)容對(duì)保證安全駕駛起到促進(jìn)作用。路面行駛質(zhì)量指數(shù)中最重要的評(píng)價(jià)標(biāo)準(zhǔn)就是道路的顛簸程度和抗滑性。道路的顛簸程度就是道路的平整度,而抗滑性能評(píng)價(jià)標(biāo)準(zhǔn)則建立在不同道路類型的基礎(chǔ)之上。本課題的研究?jī)?nèi)容是以汽車為主體的主動(dòng)探測(cè)道路信息的識(shí)別問(wèn)題。由此,本課題選取了最重要的兩種信息作為識(shí)別目標(biāo),一個(gè)是路面的顛簸程度信息,另一個(gè)是道路的類型信息。對(duì)于道路的顛簸類型判斷采用了模式識(shí)別的思想,研究采用運(yùn)動(dòng)傳感器獲取垂直于路面的加速度,通過(guò)特征提取獲取能準(zhǔn)確描述路面顛簸的類型的特征向量。通過(guò)一系列實(shí)驗(yàn)驗(yàn)證和參數(shù)調(diào)優(yōu),發(fā)現(xiàn)運(yùn)動(dòng)傳感器結(jié)合特征提取與隱馬爾可夫模型能較好的判決出道路的顛簸類型。對(duì)于道路類型的判斷上,同樣需要將道路類型具體到能準(zhǔn)確描述其信息的物理參數(shù)上。現(xiàn)階段較為成熟的識(shí)別物體的方案一般都是建立在機(jī)器視覺(jué)的基礎(chǔ)上,通過(guò)一系列實(shí)驗(yàn)驗(yàn)證了在道路類型的識(shí)別上,紋理特征能較好的表示道路的類型,最終選取的紋理特征算法為灰度共生矩陣法。同時(shí)分類器的選取方面,為了解決道路類型識(shí)別時(shí)的遮蓋問(wèn)題,文中提出了投票式支持向量機(jī)的應(yīng)用方法,并提出了完整的道路類型識(shí)別方案。論文最后為了驗(yàn)證文中提出算法與應(yīng)用方法。在道路顛簸識(shí)別方面,搭建了相應(yīng)的實(shí)驗(yàn)平臺(tái)。利用這個(gè)平臺(tái)實(shí)時(shí)采集車輛垂直方向的加速度數(shù)據(jù),并通過(guò)特征提取和分類仿真得到了滿意的效果,平均識(shí)別精確度可以達(dá)到94%。在道路類型識(shí)別方面應(yīng)用仿真,驗(yàn)證了相應(yīng)的算法和應(yīng)用。實(shí)驗(yàn)驗(yàn)證,以計(jì)算機(jī)視覺(jué)為基礎(chǔ)的紋理特征搭配改進(jìn)的支持向量機(jī)算法能較好的識(shí)別道路類型,精度上能達(dá)到較好的效果,平均識(shí)別精確度達(dá)到93.2%。總體來(lái)說(shuō)文章中所使用的算法和使用方法對(duì)于保證安全駕駛有較高的實(shí)用價(jià)值和使用價(jià)值。
[Abstract]:With the improvement of people's living standard and the decline of the overall cost of automobile, automobile plays a more and more important role in people's life. The safe driving of vehicles in complex roads is an important research direction. Road information detection is one of the key contents. However, there are few researches on road information detection, especially on vehicles as nodes to detect road information. In this paper, a multi-sensor based road information recognition algorithm is studied. The most important evaluation criteria in road driving quality index are the bumping degree and skid resistance of the road. The bumpy degree of the road is the smoothness of the road. However, the evaluation standard of anti-skid performance is based on different road types. The research content of this subject is the identification of road information with the automobile as the main body. In this paper, the most important two kinds of information are selected as the recognition target, one is the road bumping degree information, the other is the road type information. In this paper, the acceleration perpendicular to the road surface is obtained by motion sensor, and the eigenvector which can accurately describe the type of road bumps is obtained by feature extraction, which is verified by a series of experiments and optimized by parameters. It is found that the motion sensor combined with feature extraction and hidden Markov model can better judge the type of road turbulence. It is also necessary to specify the type of road to the physical parameters that can accurately describe its information. At the present stage, more mature schemes for identifying objects are generally based on machine vision. Through a series of experiments, it is proved that the texture feature can represent the road type well in road type recognition, and the final texture feature algorithm is gray-scale co-occurrence matrix method. At the same time, the selection of classifier is also discussed. In order to solve the covering problem of road type recognition, this paper presents an application method of voting support vector machine (VSVM). Finally, in order to verify the algorithm and application method in this paper, in the aspect of road bumping recognition, the paper puts forward a complete road type recognition scheme. This platform is used to collect the acceleration data of the vehicle in the vertical direction in real time, and the result is satisfactory through feature extraction and classification simulation. The average recognition accuracy can reach 94 points. The simulation is applied to road type recognition, and the corresponding algorithm and application are verified. Texture features based on computer vision and improved support vector machine (SVM) algorithm can recognize road types well and achieve better accuracy. The average recognition accuracy is 93. 2. In general, the algorithms and methods used in this paper have high practical value and use value to ensure safe driving.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U463.6;TP212

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